Graph Neural Networks for Recommender System

Tsinghua University · National University of Singapore · +1 more institution

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Abstract

Recently, graph neural network (GNN) has become the new state-of-the-art approach in many recommendation problems, with its strong ability to handle structured data and to explore high-order information. However, as the recommendation tasks are diverse and various in the real world, it is quite challenging to design proper GNN methods for specific problems. In this tutorial, we focus on the critical challenges of GNN-based recommendation and the potential solutions. Specifically, we start from an extensive background of recommender systems and graph neural networks. Then we fully discuss why GNNs are required in recommender systems and the four parts of challenges, including graph construction, network design,…

Citation impact

273
total citations
FWCI
36.98
Percentile
100%
References
23
Citations per year

Authors

4

Topics & keywords

Keywords
  • Recommender system
  • Computer science
  • Graph
  • Data science
  • Artificial neural network
  • Focus (optics)
  • Computation
  • Graph database
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